In the development of the Semantic Web researchers and industry experts agree that scalability can only be obtained by reducing standard reasoning tasks to query evaluation over (deductive) databases. From a theoretical viewpoint much has been done. Conversely, from a practical point of view, only a few reasoning services have been developed, which however typically can only deal with lightweight ontologies. To fill the gap, the paper presents owl2dlv, a modern Datalog system for evaluating SPARQL queries over very large OWL 2 knowledge bases. owl2dlv builds on the well-known ASP system dlv by incorporating novel optimizations sensibly reducing memory consumption and a server-like behavior to support multiple-query scenarios. The high potential of owl2dlv is outlined by the results of an experiment on data-intensive benchmarks, and confirmed by the interest of a major international industrial player, which has stimulated and partially supported the work.
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|Titolo:||Querying large expressive horn ontologies|
|Data di pubblicazione:||2019|
|Appare nelle tipologie:||4.1 Contributo in Atti di convegno|